Enhanced Rear Signaling (ERS) for Heavy Trucks: Mitigating Rear-end Truck Crashes Using Visual Warning Signals

In 2006, there were approximately 23,500 rear-end crashes involving heavy trucks on roadways in the United States of America. Of these crashes, 135 resulted in fatalities and 1,603 resulted in incapacitating injuries (Schaudt et al., in press). The Federal Motor Carrier Safety Administration (FMCSA) contracted with the Virginia Tech Transportation Institute (VTTI) to investigate methods to reduce or mitigate those crashes where a heavy truck has been struck from behind by another vehicle. The most prevalent contributing factor is that of the following-vehicle driver looking away, either into the vehicle interior or to the outside (but not the forward view) just prior to a crash. Most previous work on prevention of rear-end crashes has been directed toward attention-getting and eye-drawing mechanisms; that is, trying to get the following-vehicle driver to look forward at the vehicle ahead instead of continuing to look away. The Enhanced Rear Signaling (ERS) for Heavy Trucks project investigated many categories of rear-end crash countermeasures which included both visual and auditory warning signals. The purpose of introducing a visual warning signal, the focus of this paper, was to redirect the driver's attention and visual glance to the forward view. This paper will provide an overview of testing performed with visual warning signals positioned on the rear of a heavy truck trailer. These visual warning signals were tested using a static method (parked vehicles with individuals not driving) to determine how well various configurations of visual warning signals would provide improved eye-drawing capabilities. Two static experiments were performed to down-select several visual warning signal configurations prior to dynamic testing (moving vehicle with an individual driving) on the Virginia Smart Road. The results found that two ERS lighting configurations performed the best and were selected to move forward to the dynamic Smart Road tests.